AI Change Evaluation System for Deployment Validation
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Solution Overview
Problem
Existing systems face challenges in evaluating and validating change requests to a system environment effectively, leading to potential negative impacts and resource consumption when unauthorized or improperly assessed changes are deployed, due to limited data and manual processing.
Innovation Solution
A system utilizing artificial intelligence and machine learning to generate a change inference database and determine confidence scores for change requests, preventing deployment until scores meet threshold limits, and generating authentication tokens for secure implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual evaluation and validation of change requests is performed, then human expertise can assess potential impacts, but resource consumption increases and processing efficiency decreases
Solution Approach 1:
An AI-based evaluation system is introduced as an intermediary between change request submission and deployment approval. The system automatically analyzes change requests against historical data, system configurations, and impact criteria to generate risk assessments and approval recommendations, reducing reliance on manual human evaluation while maintaining reliability through automated validation processes.
Solution Approach 2:
Manual mechanical processes of human review and assessment are replaced with automated electronic systems that use machine learning algorithms, natural language processing, and data analytics to evaluate change requests. The system automatically parses change request documents, compares them against historical failure patterns, and generates confidence scores without requiring manual human intervention for each assessment.
2Reliability
If comprehensive validation of change requests is performed to prevent negative impacts, then system reliability improves, but processing time and complexity increase
Solution Approach 1:
The system performs preliminary validation and risk assessment of change requests before deployment approval is granted. By evaluating potential impacts, identifying failure points, and generating confidence scores in advance, the system ensures that only adequately validated changes proceed to deployment, preventing negative impacts while streamlining the approval process through pre-assessment.
Solution Approach 2:
The system uses historical change request data and outcomes as reference models to evaluate new change requests. By comparing proposed changes against historical patterns of successful and failed changes, the system can quickly assess risk levels and make approval decisions without requiring exhaustive validation of each individual change request from scratch.
3Productivity
If automated AI-based evaluation is implemented, then processing efficiency and productivity improve, but system complexity and data requirements increase
Solution Approach 1:
The AI evaluation system is designed to handle multiple types of change requests across different system domains using a unified evaluation framework. The system can process infrastructure changes, software deployments, configuration modifications, and other change types through the same automated pipeline, reducing the need for separate specialized systems for each change category and managing complexity through standardization.
4Measurement precision
If historical data is extensively used to generate change inference database, then evaluation accuracy improves, but data processing requirements and storage needs increase
Solution Approach 1:
The system extracts and utilizes only the most relevant features and patterns from historical change request data that are directly applicable to evaluating new change requests. By focusing on key indicators such as failure patterns, impact metrics, and successful change characteristics, the system achieves high prediction accuracy without requiring processing of all historical data in its entirety, reducing computational and storage requirements.
Data Source
AI summary
Systems, computer program products, and methods are described herein for evaluating, validating, and implementing change requests to a system environment based on artificial intelligence input. The present invention may be configured to receive a change request including a change to be made to a configuration item of a system environment, determine, based on a change inference database, potential failure points associated with deploying the change request in the system environment, and determine, based on the potential failure points, a confidence score for the change. The present invention may be configured to determine whether the confidence score for the change satisfies a threshold limit for the configuration item and prevent the change request from being deployed in the system environment until the confidence score for the change satisfies the threshold limit for the configuration item.